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Biawak-8B-Base

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qwen3

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taqiyudinadn commited on Dec 10, 2025

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----
-library_name: transformers
-tags: []
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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+library_name: transformersbase_model: Qwen/Qwen2.5-8Btags:qwenqwen2.5causal-lmcontinued-pretrainingindonesianidprddtplicense: apache-2.0language:idenBiawak-8B-BaseBiawak-8B-Base is an 8 billion parameter Large Language Model (LLM) tailored specifically for the strategic context of Indonesia. It focuses deeply on two primary pillars: Digital Space Protection (Perlindungan Ruang Digital - PRD) and Digital Talent Pool (DTP).This model is the result of Continued Pre-training (CPT) on the Qwen-8B base, further trained using a curated corpus of domain-specific Indonesian web data.Model DetailsModel DescriptionDeveloped by: [Your Organization/Developer Name]Model Type: Causal Language Model (Base)Base Model: Qwen-8B (Qwen2/2.5)Language: Indonesian (Primary), English (Secondary)License: Apache 2.0 (matches Qwen license)Training Method: Continued Pre-training (CPT) on specific domains.GoalTo provide a sovereign, domain-adapted foundation model for Indonesia that understands the nuances of digital policy laws (UU PDP/ITE), cybersecurity discourse, and the technical skill landscape of the Indonesian workforce.Dataset CompositionThe model was trained on a curated dataset with a total volume of approximately ~214.2 Million Tokens. The data strategy was designed to balance technical domain knowledge with general linguistic capabilities.Data CategoryDescriptionToken Count (M)PercentageDTP (Digital Talent Pool)Data related to digital HR, tech syllabi, career trends, certifications, and HR tech.94.0~43.9%PRD (Perlindungan Ruang Digital)Data concerning cybersecurity, PDP Law (Privacy), content moderation, digital literacy, and hoax prevention.92.0~42.9%Wikipedia IndonesiaGeneral encyclopedia data to serve as a General Knowledge Anchor and maintain grammatical coherence.28.2~13.2%Total214.2100%Intended UseAs a Base Model, the primary output is text completion. It is designed to serve as a foundation to be further fine-tuned into Chat or Instruct models.1. Digital Space Protection (PRD) DomainPolicy Sentiment Analysis: Understanding public discourse regarding data privacy policies.Misinformation Pattern Detection: Recognizing sentence structures often used in spreading hoaxes or online scams within the Indonesian context.Digital Legal Terminology: Deep understanding of terminologies related to UU ITE (Electronic Information and Transactions Law) and UU PDP (Personal Data Protection Law).2. Digital Talent Pool (DTP) DomainSkill Gap Analysis: Mapping programming or digital marketing skill needs based on Indonesian job market trends.Curriculum Development: Assisting in structuring relevant technology training syllabi.Talent Matching: Understanding job descriptions and digital talent profiles.How to Get StartedYou can load this model using the transformers library.import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# 1. Configuration
+model_id = "YOUR_USERNAME/Biawak-8B-Base" # Replace with your actual Hub ID
+# 2. Load Model
+# Loading in bfloat16 is recommended for A100/A10G, use float16 for T4
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# 3. Inference Example (Completion)
+# Context: Digital Talent Pool
+input_text = "Strategi utama untuk mengurangi gap talenta digital di Indonesia adalah"
+inputs = tokenizer(input_text, return_tensors="pt").to("cuda")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=100, do_sample=True, temperature=0.7)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+Training DetailsTraining ProcedureThe model underwent Continued Pre-training using a causal language modeling objective. The training process focused on injecting domain-specific knowledge (PRD & DTP) while preserving the reasoning capabilities of the original Qwen model.Hardware & EnvironmentHardware: NVIDIA A100 80GB (Google Colab Pro+)Training Duration: ~36 HoursFrameworks: PyTorch, Transformers, AccelerateHyperparameters (Highlights)Sequence Length: 4096 (or 8192 depending on your specific run)Optimizer: AdamWLearning Rate Schedule: Cosine DecayPrecision: bf16 (bfloat16)LimitationsBase Model Status: This model has not undergone Instruction Tuning (SFT) or RLHF. Users may need to provide few-shot prompting to achieve desired results or perform further fine-tuning.Web Data Bias: Since it utilizes crawled data (94M + 92M tokens), the model may inherit biases found in web articles or discussion forums related to digital issues in Indonesia.Hallucinations: Like all LLMs, Biawak-8B can generate plausible-sounding but factually incorrect information, especially regarding specific legal cases.RecommendationsIt is highly recommended to perform Supervised Fine-Tuning (SFT) using high-quality instruction datasets (Q&A) in the PRD and DTP fields before deploying this model in production applications, such as Public Service Chatbots or HR Assistants.